Sequential Bayesian Inference for Dynamic State Space Model Parameters

Accurate characterizations of behavior during learning experiments are essential for understanding the neural bases of learning. Whereas learning experiments often give subjects multiple tasks to learn simultaneously, most analyze subject performance separately on each individual task. This analysis strategy ignores the true interleaved presentation order of the tasks and cannot distinguish learning behavior from response preferences that may represent a subject's biases or strategies. We present a Bayesian analysis of a state-space model for characterizing simultaneous learning of multiple tasks and for assessing behavioral biases in learning experiments with interleaved task presentations. Under the Bayesian analysis the posterior probability densities of the model parameters and the learning state are computed using Monte Carlo Markov Chain methods. Measures of learning, including the learning curve, the ideal observer curve, and the learning trial translate directly from our previous likelihood-based state-space model analyses. We compare the Bayesian and current likelihood–based approaches in the analysis of a simulated conditioned T-maze task and of an actual object–place association task. Modeling the interleaved learning feature of the experiments along with the animal's response sequences allows us to disambiguate actual learning from response biases. The implementation of the Bayesian analysis using the WinBUGS software provides an efficient way to test different models without developing a new algorithm for each model. The new state-space model and the Bayesian estimation procedure suggest an improved, computationally efficient approach for accurately characterizing learning in behavioral experiments.

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Adaptive Model Predictive Vibration Control of a Cantilever Beam with Real-Time Parameter Estimation

Shock and Vibration ◽

10.1155/2014/741765 ◽

2014 ◽

Vol 2014 ◽

pp. 1-15 ◽

Cited By ~ 10

Author(s):

Gergely Takács ◽

Tomáš Polóni ◽

Boris Rohal’-Ilkiv

Keyword(s):

Control System ◽

Vibration Control ◽

State Space ◽

Real Time ◽

Cantilever Beam ◽

State Space Model ◽

Model Parameters ◽

Extended Kalman Filtering ◽

Space Model ◽

Continuous State

This paper presents an adaptive-predictive vibration control system using extended Kalman filtering for the joint estimation of system states and model parameters. A fixed-free cantilever beam equipped with piezoceramic actuators serves as a test platform to validate the proposed control strategy. Deflection readings taken at the end of the beam have been used to reconstruct the position and velocity information for a second-order state-space model. In addition to the states, the dynamic system has been augmented by the unknown model parameters: stiffness, damping constant, and a voltage/force conversion constant, characterizing the actuating effect of the piezoceramic transducers. The states and parameters of this augmented system have been estimated in real time, using the hybrid extended Kalman filter. The estimated model parameters have been applied to define the continuous state-space model of the vibrating system, which in turn is discretized for the predictive controller. The model predictive control algorithm generates state predictions and dual-mode quadratic cost prediction matrices based on the updated discrete state-space models. The resulting cost function is then minimized using quadratic programming to find the sequence of optimal but constrained control inputs. The proposed active vibration control system is implemented and evaluated experimentally to investigate the viability of the control method.

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Dynamic state-space model and performance analysis for solar active walls embedded phase change material

Sustainable Energy Grids and Networks ◽

10.1016/j.segan.2020.100401 ◽

2020 ◽

Vol 24 ◽

pp. 100401

Author(s):

Lahoucine Ouhsaine ◽

Harry Ramenah ◽

Mohammed El Ganaoui ◽

Abdelaziz Mimet

Keyword(s):

Performance Analysis ◽

Phase Change ◽

State Space ◽

Phase Change Material ◽

State Space Model ◽

Dynamic State ◽

Space Model ◽

And Performance ◽

Change Material

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Optimal identification for dynamic state space model of FOG in engineering application

2016 IEEE International Conference on Mechatronics and Automation ◽

10.1109/icma.2016.7558827 ◽

2016 ◽

Author(s):

Peng Liu ◽

Weiquan Huang ◽

Fengzhao Sun

Keyword(s):

State Space ◽

State Space Model ◽

Engineering Application ◽

Dynamic State ◽

Space Model

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A New Dynamic State Space Model for the Cylindrical Grinding Process

Dynamic Systems and Control, Parts A and B ◽

10.1115/imece2005-82073 ◽

2005 ◽

Cited By ~ 1

Author(s):

Cheol W. Lee

Keyword(s):

Surface Roughness ◽

State Space ◽

State Space Model ◽

Dynamic State ◽

Grinding Process ◽

Cylindrical Grinding ◽

Space Model ◽

Proposed Model ◽

Grinding Power ◽

Actual Part

A new dynamic state space model is proposed for the in-process estimation and prediction of part qualities in the plunge cylindrical grinding process. A through review on various grinding models in literature reveals a hidden dynamic relationship among the grinding conditions, the grinding power, the surface roughness, and the part size due to the machine dynamics and the wheel wear, based on which a nonlinear state space equation is derived. After the model parameters are determined according to the reported values in literature, several simulations are run to verify that the model makes good physical sense. Since some of the output variables, such as the actual part size, may or may not be measured in industry applications, the observability is tested for different sets of outputs in order to see how each set of on-line sensors affects the observability of the model. The proposed model opens a new way of estimating the part qualities such as the surface roughness and the actual part size based on application of the state estimation algorithm to the measured outputs such as the grinding power. In addition, a long term prediction of the part qualities in batch grinding processes would be realized by simulation of the proposed model. Possible applications to monitoring and control of grinding processes are discussed along with several technical challenges lying ahead.

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